System and method for rules driven data record reduction

ABSTRACT

According to some embodiments, data is received indicative of a plurality of insurance claims submitted in connection with insurance policies. A rules driven claim processing engine may then apply a first exclusion filter to the received plurality of claims, wherein the first exclusion filter operates to remove claims from the received plurality of claims to create a first subset of claims. A second exclusion filter may then be applied to the first subset of claims, wherein the second exclusion filter operates to remove claims from the first subset of claims to create a second subset of claims. It may then be automatically arranged for the second subset of claims to be processed via an expedited claim processing workflow.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of co-pending U.S. patentapplication Ser. No. 14/051,703, entitled System and Method for RulesDriven Insurance Claim Processing, filed Oct. 11, 2013, the contents ofwhich are hereby incorporated herein by reference in their entirety forall purposes.

FIELD

The present invention relates to computer systems and more particularlyto computer systems that provide rules driven insurance claimprocessing.

BACKGROUND

An insurer may provide payments when claims are made in connection withan insurance policy. For example, an employee who is injured whileworking might receive payments associated with a workers' compensationinsurance policy purchased by his or her employer. Similarly, a personinvolved in an automobile accident may receive a payment in connectionwith an automobile insurance policy. The insurer may assign a claimhandler to communicate with a claimant, an employer, another insurer,and/or medical service providers to help determine the appropriateamount of payment. Note that submitted claims may involve variousamounts of work by a claim handler. For example, one submitted insuranceclaim might be relatively straightforward while another claim involvescomplex determinations of liability and/or injury issues.

In one approach, a received insurance claim is assigned to a claimhandler in a random or round robin manner. This, however, might lead toone claim handler having a significantly more complex workload ascompared to another claim handler. Moreover, manually determining whichclaim handler should be assigned to each individual insurance claim canbe time consuming task, especially when there are a substantial numberof claims to be analyzed. For example, an insurer might receive tens ofthousands of new insurance claims each year (which might represent abillion dollars of potential liability). It would therefore be desirableto provide systems and methods to facilitate the assignment of insuranceclaims to claim handlers, in an automated, efficient, and accuratemanner.

SUMMARY

According to some embodiments, systems, methods, apparatus, computerprogram code and means may facilitate the assignment of insurance claimsto claim handlers. In some embodiments, a communication device mayreceive data indicative of a plurality of insurance claims submitted inconnection with insurance policies. A first exclusion filter may beapplied to the received plurality of claims, wherein the first exclusionfilter operates to remove claims from the received plurality of claimsto create a first subset of claims. Moreover, a second exclusion filtermay be applied to the first subset of claims, wherein the secondexclusion filter operates to remove claims from the first subset ofclaims to create a second subset of claims. It may then be automaticallyarranged for the second subset of claims to be processed via anexpedited claim processing workflow.

A technical effect of some embodiments of the invention is an improvedand computerized method to facilitate the assignment of insurance claimsto claim handlers. With these and other advantages and features thatwill become hereinafter apparent, a more complete understanding of thenature of the invention can be obtained by referring to the followingdetailed description and to the drawings appended hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram of a system according to some embodiments of thepresent invention.

FIG. 2 illustrates a method that might be performed in accordance withsome embodiments.

FIGS. 3A and 3B are examples of methods that might be performedaccording to some embodiments.

FIG. 4 is block diagram of a rules driven insurance claim processingtool or platform according to some embodiments of the present invention.

FIG. 5 is a tabular portion of an insurance claim database according tosome embodiments.

FIG. 6 is a graph illustrating an overall number of insurance claimsthat may be potentially eligible for processing via an expeditedworkflow in accordance with some embodiments.

FIG. 7 is an insurance claim process flow in accordance with someembodiments.

FIG. 8 is a partially functional block diagram that illustrates aspectsof a computer system provided in accordance with some embodiments of theinvention.

FIG. 9 is a block diagram that provides another representation ofaspects of the system of FIG. 8.

FIG. 10 is a flow chart illustrating how a predictive model might betrained according to some embodiments.

FIG. 11 illustrates predictive model inputs according to someembodiments.

DETAILED DESCRIPTION

An insurer may provide payments when claims are made in connection withan insurance policy, such as a workers' compensation or automobileinsurance policy. Note that embodiments may also be associated withother types of insurance, including long term disability insurance,short term disability insurance, and/or flexible combinations of shortand long term disability insurance.

Manually determining which claim handler should be assigned to eachindividual insurance claim can be time consuming and difficult task,especially when there are a substantial number of claims to be analyzed.It would therefore be desirable to provide systems and methods tofacilitate the assignment of insurance claims to claim handlers. FIG. 1is block diagram of a system 100 according to some embodiments of thepresent invention. In particular, the system 100 includes a rules drivenclaim processing engine 150 that receives information about insuranceclaims (e.g., by receiving an electronic file from a team leader, anemployer, an employee, an insurance agent, a medical service provider,or a data storage unit 110). According to some embodiments, incomingtelephone calls and/or documents from a doctor may be used to createinformation in a claim system 120 which, in turn, can provideinformation to the rules driven claim processing engine 150. In otherembodiments, the rules driven claim processing engine 150 may retrieveinformation from a data warehouse 130 (e.g., when the rules driven claimprocessing engine 150 is associated with an automobile insurance system,some information may be copied from an automobile insurance datawarehouse). In other embodiments, some or all of the information aboutan insurance claim may be received via a claim submission process. Therules driven claim processing engine 150 may, according to someembodiments, help identify insurance claims that can be processed via anexpedited workflow. According to some embodiments, historicalinformation may be used to generate appropriate claim processing rule tobe applied based on the specific facts of the insurance claim beingprocessed.

The rules driven claim processing engine 150 might be, for example,associated with a Personal Computers (PC), laptop computer, anenterprise server, a server farm, and/or a database or similar storagedevices. The rules driven claim processing engine 150 may, according tosome embodiments, be associated with an insurance provider.

According to some embodiments, an “automated” rules driven claimprocessing engine 150 may facilitate the assignment of insurance claimsto claim handlers 160. For example, the rules driven claim processingengine 150 may automatically output a recommended claim classificationfor a received insurance claim (e.g., to a team leader) which may thenbe used to facilitate assignment of a claim handler 160. As used herein,the term “automated” may refer to, for example, actions that can beperformed with little (or no) intervention by a human.

As used herein, devices, including those associated with the rulesdriven claim processing engine 150 and any other device describedherein, may exchange information via any communication network which maybe one or more of a Local Area Network (LAN), a Metropolitan AreaNetwork (MAN), a Wide Area Network (WAN), a proprietary network, aPublic Switched Telephone Network (PSTN), a Wireless ApplicationProtocol (WAP) network, a Bluetooth network, a wireless LAN network,and/or an Internet Protocol (IP) network such as the Internet, anintranet, or an extranet. Note that any devices described herein maycommunicate via one or more such communication networks.

The rules driven claim processing engine 150 may store information intoand/or retrieve information from the data storage 110. The data storage110 might be associated with, for example, a client, an employer, orinsurance policy and might store data associated with past and currentinsurance claims and/or payments. The data storage 110 may be locallystored or reside remote from the insurance claim rules driven claimprocessing engine 150. As will be described further below, the datastorage 110 may be used by the rules driven claim processing engine 150to generate predictive models. According to some embodiments, the rulesdriven claim processing engine 150 communicates a recommended claimprocessing workflow (e.g., expedited or normal workflows), such as bytransmitting an electronic file to a claim handler 160, a client device,an insurance agent or analyst platform, an email server, a workflowmanagement system, etc. In other embodiments, the rules driven claimprocessing engine 150 might output a recommended claim workflowindication to a team leader who might select a claim handler based onthat indication or override the indication based on other factorsassociated with the insurance claim.

Although a single rules driven claim processing engine 150 is shown inFIG. 1, any number of such devices may be included. Moreover, variousdevices described herein might be combined according to embodiments ofthe present invention. For example, in some embodiments, the claim rulesdriven claim processing engine 150 and data storage 110 might beco-located and/or may comprise a single apparatus.

FIG. 2 illustrates a method that might be performed by some or all ofthe elements of the system 100 described with respect to FIG. 1according to some embodiments of the present invention. The flow chartsdescribed herein do not imply a fixed order to the steps, andembodiments of the present invention may be practiced in any order thatis practicable. Note that any of the methods described herein may beperformed by hardware, software, or any combination of these approaches.For example, a computer-readable storage medium may store thereoninstructions that when executed by a machine result in performanceaccording to any of the embodiments described herein.

At 202, data may be received indicative of a plurality of insuranceclaims submitted in connection with insurance policies. The insuranceclaims might be associated with, for example, workers' compensationinsurance claims and/or automobile insurance claims. Note that the dataindicative of insurance claims might be received via submitted paperclaims, a telephone call center, and/or an online claim submission webpage.

At 204, a first “exclusion filter” may be applied to the receivedplurality of claims. The first exclusion filter may, for example,operate to remove claims from the received plurality of claims to createa first subset of claims. Similarly, at 206 a second exclusion filtermay then be applied to the first subset of claims. The second exclusionfilter may operate to remove additional claims from the first subset ofclaims to create a second subset of claims.

These exclusion filters may help identify which claims are relativelystraightforward and, as a result, may be eligible to be handled by anexpedited claim processing workflow. According to some embodiments, anexclusion filter might be associated with an insurance policy typeassociated with an insurance claim. For example, “commercial” insurancepolicies may be inherently more complex as compared to “personal”insurance policies and, as a result, may be automatically excluded fromthe expedited workflow. As another example, an exclusion filter mightrepresent a rule based on a geographic location associated with aninsurance claim (e.g., claims arising from accidents that occurred in aparticular jurisdiction may involve additional processing steps thatmake them ineligible for an expedited workflow).

According to some embodiments, an exclusion filter may represent a rulebased on a threshold monetary amount associated with an insurance claim.For example, a claim might be eligible for expedited processing if itdoes not represent a substantial amount of potential liability. Asanother example, an exclusion filter may represent a rule based on howdifficult it will be to ascertain liability associated with an insuranceclaim and/or an injury associated with an insurance claim. For example,an insurance claim where it is obvious who is at fault (e.g., when oneautomobile involved in an accident was parked at the time the accidentoccurred) and the type of injury is relatively straightforward may beeligible for expedited processing. As still another example, anexclusion filter might comprise a rule such that all submittedinsurances claim having at least one party who has already obtainedlegal representation may be ineligible for expedited processing. Anexclusion filter may represent a rule based on particular types ofinjuries. For example, relatively series injuries (e.g., back injuries,injuries to internal organs, etc.) may be assumed to be ineligible forexpedited processing because of the need to review medical reports,contact healthcare providers, etc.

At 208, it may be automatically arranged for at least some of the secondsubset of claims to be processed via an expedited claim processingworkflow. That is, the relatively complex and/or difficult claims mayhave been automatically excluded from the second subset (via theexclusion filters) and the remaining claims may be assigned to claimhandlers and/or processes that can quickly settle the matters. Accordingto some embodiments, information about the second subset of claims(e.g., a list of identifiers) may be automatically transmitted to anemail server, a workflow application, a report generator, and/or acalendar application. An indication of the second set of claims may alsobe output to a team leader and/or automatically routed to apre-determined claim handler or team associated with an expedited claimprocessing workflow. According to some embodiments, the expeditedworkflow may be associated with a straight through claim process (e.g.,a claim could be automatically paid by the system rather than anadjuster if an estimate meets specified criteria).

Consider, for example, FIG. 3A which illustrates one method that mightbe performed by some or all of the elements of the system 100 describedwith respect to FIG. 1 according to some embodiments of the presentinvention. In this example, data is received at 302 indicative of aplurality of workers' compensation insurance claims submitted inconnection with insurance policies. The data may be received, forexample, in substantially real time or on a periodic basis (e.g., abatch of submitted claims might be received on a daily basis).

Exclusion filters may then be applied to the received claims. Inparticular, a claim compensability exclusion filter might be applied tothe received claims at 304. The claim compensability exclusion filtermight evaluate, for example, whether or not the claim is associated witha valid workers' compensation insurance policy. Claims that are notimmediately associated with a valid insurance policy may be ineligiblefor expedited processing and may instead be processed via a normalworkflow at 306 (e.g., the claim handler may further investigate theclaim).

A geographic location exclusion filter may then be applied to theremaining workers' compensation insurance claims at 308. For example,claims associated with insurance policies and/or accidents that occurredin Michigan or Hawaii might be identified at 308 and assigned to anormal insurance claim processing workflow at 306. Similarly, claimsassociated with a particular types of injuries may be identified at 310and assigned to the normal insurance claim processing workflow at 306(e.g., because those types of injuries may make expedited processingimpractical). The remaining claims may therefore be eligible for anexpedited workflow at 312. Although only three exclusion filters areillustrated in FIG. 3A, note that any number of such exclusion filtersmay be applied as appropriate. For example, exclusion filters might beassociated with workers' compensation claims that are associated withdisabilities, a possibility of indemnity payments, and/or issues ofcompensability.

Now consider FIG. 3B, which illustrates another method that might beperformed by some or all of the elements of the system 100 describedwith respect to FIG. 1 according to some embodiments of the presentinvention. In this example, data is received at 314 indicative of aplurality of automobile insurance claims submitted in connection withinsurance policies. Exclusion filters may then be applied to thereceived claims. In particular, a claim complexity exclusion filtermight be applied to the received claims at 316. The claim complexityexclusion filter might evaluate, for example, whether or not the claimis associated with an unclear liability determination, commercialproperty, etc. Claims that are too complex may be ineligible forexpedited processing and may instead be processed via a normal workflowat 318 (e.g., the claim handler may further investigate the claim).

A total loss amount exclusion filter may then be applied to theremaining automobile insurance claims at 320. For example, claimsassociated with loss amounts over a pre-determined threshold value mightbe identified at 320 and assigned to a normal insurance claim processingworkflow at 318. According to some embodiments, claims associated with avehicle classified as a “total loss” (that is, the damage to the vehicleas a result of an accident cannot be repaired) may be eliminated. Theclaims remaining after 320 may represent the set of claims that areeligible for an expedited workflow at 322.

Thus, application of the exclusion filters may reduce a received set ofclaims by sequentially removing potentially difficult or complex claimsfrom the pool that may be eligible for expedited processing. Note thatthe embodiments described herein may be implemented using any number ofdifferent hardware configurations. For example, FIG. 4 illustrates arules driven claim processing platform 400 that may be, for example,associated with the system 100 of FIG. 1. The rules driven claimprocessing platform 400 comprises a processor 410, such as one or morecommercially available Central Processing Units (CPUs) in the form ofone-chip microprocessors, coupled to a communication device 420configured to communicate via a communication network (not shown in FIG.4). The communication device 420 may be used to communicate, forexample, with one or more claim systems, remote team leaders, and/orclaim handler devices. The rules driven claim processing platform 400further includes an input device 440 (e.g., a mouse and/or keyboard toenter information about exclusion filters or rules) and an output device450 (e.g., to output a recommended subset of insurance claims forexpedited handling).

The processor 410 also communicates with a storage device 430. Thestorage device 430 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, mobile telephones, and/orsemiconductor memory devices. The storage device 430 stores a program412 and/or rules driven claim processing logic 414 for controlling theprocessor 410. The processor 410 performs instructions of the programs412, 414, and thereby operates in accordance with any of the embodimentsdescribed herein. For example, the processor 410 may receive dataindicative of a plurality of insurance claims submitted in connectionwith insurance policies. The processor 410 may then apply a firstexclusion filter to the received plurality of claims, wherein the firstexclusion filter operates to remove claims from the received pluralityof claims to create a first subset of claims. A second exclusion filtermay then be applied by the processor 410 to the first subset of claims,wherein the second exclusion filter operates to remove claims from thefirst subset of claims to create a second subset of claims. Theprocessor 410 may then automatically arrange for the second subset ofclaims to be processed via an expedited claim processing workflow.

The programs 412, 414 may be stored in a compressed, uncompiled and/orencrypted format. The programs 412, 414 may furthermore include otherprogram elements, such as an operating system, a database managementsystem, and/or device drivers used by the processor 410 to interfacewith peripheral devices.

As used herein, information may be “received” by or “transmitted” to,for example: (i) the rules driven claim processing platform 400 fromanother device; or (ii) a software application or module within therules driven claim processing platform 400 from another softwareapplication, module, or any other source.

In some embodiments (such as shown in FIG. 4), the storage device 430further stores an insurance claim database 500, claim handler data 460(e.g., indicating which handlers specialize in expedited claimprocessing), and exclusion filter rules 470. An example of a databasethat may be used in connection with the rules driven claim processingplatform 400 will now be described in detail with respect to FIG. 5.Note that the database described herein is only one example, andadditional and/or different information may be stored therein. Moreover,various databases might be split or combined in accordance with any ofthe embodiments described herein. For example, the claim handler data460 and/or exclusion filter rules 470 might be combined and/or linked toeach other within the rules driven claim processing application 414.

Referring to FIG. 5, a table is shown that represents the insuranceclaim database 500 that may be stored at the rules driven claimprocessing platform 400 according to some embodiments. The table mayinclude, for example, entries identifying insurance claims submittedunder insurance policies. The table may also define fields 502, 504,506, 508, 510 for each of the entries. The fields 502, 504, 506, 508,510 may, according to some embodiments, specify: a claim identifier 502,an insurance type 504, a jurisdiction 506, an indication of whether ornot the claim is excluded from an expedited workflow 508, and anassigned claim handler identifier 510. The insurance claim database 500may be created and updated, for example, based on informationelectrically received on a periodic basis.

The claim identifier 502 may be, for example, a unique alphanumeric codeidentifying a claim submitted in connection with an insurance policy.The insurance type 504 and jurisdiction 506 may represent informationassociated with a particular claim. Based on the insurance type 504 andjurisdiction 506, it may be determined whether or not the claim shouldbe excluded from an expedited workflow 508. In the example of FIG. 5,claims associated with an insurance type 504 of “commercial” are to beexcluded from the expedited workflow. In addition, claims associatedwith Michigan or Hawaii are to be excluded from the expedited workflow.As a result, claim “C_100002” is excluded (because it is “commercial”)and claim “C_100003” is excluded (because it is associated withMichigan) while the other claims are not excluded. The non-excludedclaims may be assigned to claim handler identifier 510 “H_101” whospecializes in the expedited insurance claim processing. Althoughparticular data elements are illustrated in FIG. 5, note that the claimdatabase 500 could include any other type of information in addition toor instead of the identified elements. For example, the claim database500 might include an injury type or description or an indication thatvehicle is considered a “total loss” (e.g., the damage cannot bepractically repaired).

Thus, embodiments may be associated with a series of exclusion filtersto help identify which claims may be appropriately handled via anexpedited workflow. For example, FIG. 6 is a graph 600 illustrating anoverall number of insurance claims that may be potentially eligible forprocessing via an expedited workflow in accordance with someembodiments. Initially, a set of insurance claim 612 is received and allmight be considered as being in a pool of claims that are potentiallyeligible for expedited processing. A first filter is then applied andacts to remove some of those claims from that pool creating a reducedsubset of claims 614. Further filters may be applied as appropriateuntil a final subset of claims 616 is identified. Those claims may thenbe handled via the expedited workflow.

Consider a set C representing all received insurance claims {c₁, c₂, . .. c_(n)}. According to some embodiments, any techniques may be used toidentify the subset of those claims that are eligible for expeditedprocessing. For example, the following subset might be identified:all claims in C that ∉ complex and ∉ invalidwhere “complex” refers to the subset of claims that have complexityissues making them ineligible for expedited processing (e.g., seriousinjuries, difficult determination of liability) and “invalid” refers tothe subset of claims that may have compensability issues (e.g., it mightnot be clear that the claim is covered by a valid insurance policy).Note that such identification might be performed in any number ofdifferent ways. In some embodiments described herein a sequence of“exclusion filters” are utilized. In some cases, “inclusion filters”might be utilized instead or in addition to exclusion filters. As otherexamples, a script or Structured Query Language (“SQL”) protocoloperation might be used to identify appropriate claims.

FIG. 7 is an insurance claim process flow 700 in accordance with someembodiments. The flow 700 begins in connection with a First Notice OfLoss (“FNOL”) event 710. The FNOL event 710 may be associated with thecollection of information, including vehicle information, partyinformation, contact information, preferred types of communicationchannels, and detailed facts about the loss. It may then be determinedat 720 (e.g., via the application of exclusion filters and rules)whether or not the claim is eligible for expedited workflow processing.If the claim is not eligible at 720, it may undergo a normal claimhandling process at 730.

If the claim is eligible at 720, the expedited workflow processingbegins at 740. The expedited workflow processing 740 may include, forexample, contacting an insured to verify facts about the loss, coverage,and/or liability associated with claim. The insured may be provided withexpectations about the next steps in the process and one or more thirdparties may be contacted. According to some embodiments, informationabout an estimate (e.g., associated with an automobile repair) may bedetermined at 750. If the value of the estimate is below apre-determined threshold value at 760, payment can be made at 770 (andthe claim file may eventually be closed by the handler). If the value ofthe estimate is not below the predetermined threshold value at 760,additional steps may be scheduled at 780 before payment is made at 770.The additional steps 780 may involve getting more estimates and/orhaving the claim handler review information about the claim in moredetail.

Thus, embodiments may provide an efficient and automated ability toidentify insurance claims that are eligible for expedited processing.According to some embodiments, a determination regarding expeditedprocessing may be based at least in part on rules created by apredictive model trained with historical insurance claim information.For example, the creation of exclusion filters might be aided by datamodeling, input from an insurer's claim subject matter experts, andanalysis of historical claim experience. The following are somevariables that might be used by a predictive model to help identifyappropriate exclusion filters:

-   -   Date of birth,    -   Injury/diagnosis,    -   Salary, and    -   Location of accident.

According to some embodiments, the predictive model utilizes differentgroupings associated with different sets and/or weights of relevantfactors. For example, depending on high level grouping, differentvariables may be significant and/or relevant and the weightings ofcommon variables may be different.

In general, and for the purposes of introducing concepts of embodimentsof the present invention, a computer system may incorporate a“predictive model.” As used herein, the phrase “predictive model” mightrefer to, for example, any of a class of algorithms that are used tounderstand relative factors contributing to an outcome, estimate unknownoutcomes, discover trends, and/or make other estimations based on a dataset of factors collected across prior trials. Note that a predictivemodel might refer to, but is not limited to, methods such as ordinaryleast squares regression, logistic regression, decision trees, neuralnetworks, generalized linear models, and/or Bayesian models. Thepredictive model is trained with historical claim transaction data, andis applied to current claim transactions to determine how the currentclaim transactions should be handled. Both the historical claimtransaction data and data representing the current claim transactionsmight include, according to some embodiments, indeterminate data orinformation extracted therefrom. For example, such data/information maycome from narrative and/or medical text notes associated with a claimfile.

Features of some embodiments associated with a predictive model will nowbe described by first referring to FIG. 8. FIG. 8 is a partiallyfunctional block diagram that illustrates aspects of a computer system800 provided in accordance with some embodiments of the invention. Forpresent purposes it will be assumed that the computer system 800 isoperated by an insurance company (not separately shown) for the purposeof referring certain claims to insurance claim workflows and/or handlersas appropriate.

The computer system 800 includes a data storage module 802. In terms ofits hardware the data storage module 802 may be conventional, and may becomposed, for example, by one or more magnetic hard disk drives. Afunction performed by the data storage module 802 in the computer system800 is to receive, store and provide access to both historical claimtransaction data (reference numeral 804) and current claim transactiondata (reference numeral 806). As described in more detail below, thehistorical claim transaction data 804 is employed to train a predictivemodel to provide an output that indicates how a claim should by handledprogram (e.g., expedited or non-expedited processing), and the currentclaim transaction data 806 is thereafter analyzed by the predictivemodel. Moreover, as time goes by, and results become known fromprocessing current claim transactions, at least some of the currentclaim transactions may be used to perform further training of thepredictive model. Consequently, the predictive model may thereby adaptitself to changing claim patterns.

Either the historical claim transaction data 804 or the current claimtransaction data 806 might include, according to some embodiments,determinate and indeterminate data. As used herein and in the appendedclaims, “determinate data” refers to verifiable facts such as the dateof birth, age or name of a claimant or name of another individual or ofa business or other entity; a type of injury, accident, sickness, orpregnancy status; a medical diagnosis; a date of loss, or date of reportof claim, or policy date or other date; a time of day; a day of theweek; a vehicle identification number, a geographic location; and apolicy number.

As used herein and in the appended claims, “indeterminate data” refersto data or other information that is not in a predetermined formatand/or location in a data record or data form. Examples of indeterminatedata include narrative speech or text, information in descriptive notesfields and signal characteristics in audible voice data files.Indeterminate data extracted from medical notes or accident reportsmight be associated with, for example, an amount of loss and/or detailsabout how an accident occurred.

The determinate data may come from one or more determinate data sources808 that are included in the computer system 800 and are coupled to thedata storage module 802. The determinate data may include “hard” datalike the claimant's name, date of birth, social security number, policynumber, address; the date of loss; the date the claim was reported, etc.One possible source of the determinate data may be the insurancecompany's policy database (not separately indicated). Another possiblesource of determinate data may be from data entry by the insurancecompany's claims intake administrative personnel.

The indeterminate data may originate from one or more indeterminate datasources 810, and may be extracted from raw files or the like by one ormore indeterminate data capture modules 812. Both the indeterminate datasource(s) 810 and the indeterminate data capture module(s) 812 may beincluded in the computer system 800 and coupled directly or indirectlyto the data storage module 802. Examples of the indeterminate datasource(s) 810 may include data storage facilities for document images,for text files (e.g., claim handlers' notes) and digitized recordedvoice files (e.g., claimants' oral statements, witness interviews, claimhandlers' oral notes, etc.). Examples of the indeterminate data capturemodule(s) 812 may include one or more optical character readers, aspeech recognition device (i.e., speech-to-text conversion), a computeror computers programmed to perform natural language processing, acomputer or computers programmed to identify and extract informationfrom narrative text files, a computer or computers programmed to detectkey words in text files, and a computer or computers programmed todetect indeterminate data regarding an individual. For example, claimhandlers' opinions may be extracted from their narrative text filenotes.

The computer system 800 also may include a computer processor 814. Thecomputer processor 814 may include one or more conventionalmicroprocessors and may operate to execute programmed instructions toprovide functionality as described herein. Among other functions, thecomputer processor 814 may store and retrieve historical claimtransaction data 804 and current claim transaction data 806 in and fromthe data storage module 802. Thus the computer processor 814 may becoupled to the data storage module 802.

The computer system 800 may further include a program memory 816 that iscoupled to the computer processor 814. The program memory 816 mayinclude one or more fixed storage devices, such as one or more hard diskdrives, and one or more volatile storage devices, such as RAM (randomaccess memory). The program memory 816 may be at least partiallyintegrated with the data storage module 802. The program memory 816 maystore one or more application programs, an operating system, devicedrivers, etc., all of which may contain program instruction steps forexecution by the computer processor 814.

The computer system 800 further includes a predictive model component818. In certain practical embodiments of the computer system 800, thepredictive model component 818 may effectively be implemented via thecomputer processor 814, one or more application programs stored in theprogram memory 816, and data stored as a result of training operationsbased on the historical claim transaction data 804 (and possibly alsodata resulting from training with current claims that have beenprocessed). In some embodiments, data arising from model training may bestored in the data storage module 802, or in a separate data store (notseparately shown). A function of the predictive model component 818 maybe to determine an appropriate workflow for current claim transactions.The predictive model component may be directly or indirectly coupled tothe data storage module 802.

The predictive model component 818 may operate generally in accordancewith conventional principles for predictive models, except, as notedherein, for at least some of the types of data to which the predictivemodel component is applied. Those who are skilled in the art aregenerally familiar with programming of predictive models. It is withinthe abilities of those who are skilled in the art, if guided by theteachings of this disclosure, to program a predictive model to operateas described herein.

Still further, the computer system 800 includes a model trainingcomponent 820. The model training component 820 may be coupled to thecomputer processor 814 (directly or indirectly) and may have thefunction of training the predictive model component 818 based on thehistorical claim transaction data 804. (As will be understood fromprevious discussion, the model training component 820 may further trainthe predictive model component 818 as further relevant claim transactiondata becomes available.) The model training component 820 may beembodied at least in part by the computer processor 814 and one or moreapplication programs stored in the program memory 816. Thus the trainingof the predictive model component 818 by the model training component820 may occur in accordance with program instructions stored in theprogram memory 816 and executed by the computer processor 814.

In addition, the computer system 800 may include an output device 822.The output device 822 may be coupled to the computer processor 814. Afunction of the output device 822 may be to provide an output that isindicative of (as determined by the trained predictive model component818) particular exclusion filter rules for the current claimtransactions. The output may be generated by the computer processor 814in accordance with program instructions stored in the program memory 816and executed by the computer processor 814. More specifically, theoutput may be generated by the computer processor 814 in response toapplying the data for the current claim transaction to the trainedpredictive model component 818. The output may, for example, be atrue/false flag or a number within a predetermined range of numbers. Insome embodiments, the output device may be implemented by a suitableprogram or program module executed by the computer processor 814 inresponse to operation of the predictive model component 818.

Still further, the computer system 800 may include a rule routing module824. The rule routing module 824 may be implemented in some embodimentsby a software module executed by the computer processor 814. The rulerouting module 824 may have the function of directing workflow based onthe output from the output device. Thus the rule routing module 824 maybe coupled, at least functionally, to the output device 822. In someembodiments, for example, the rule routing module 824 may directworkflow by referring, to a claim handler 826, current claimtransactions analyzed by the predictive model component 818 and found tobe associated with one or more exclusion or inclusion filters. Inparticular, these current claim transactions may be referred to casemanager 828 who is associated with the claim handler 826. The claimhandler 826 may be a part of the insurance company that operates thecomputer system 800, and the case manager 828 might be an employee ofthe insurance company.

FIG. 9 is another block diagram that presents a computer system 900 in asomewhat more expansive or comprehensive fashion (and/or in a morehardware-oriented fashion). The computer system 900, as depicted in FIG.9, includes a “rules driven claim processing engine” 901 toautomatically and selectively assess newly received insurance claims forthe insurance company. As seen from FIG. 9, the computer system 900 mayfurther include a conventional data communication network 902 to whichthe rules driven claim processing engine 901 is coupled.

FIG. 9 also shows, as parts of computer system 900, data input device(s)904 and data source(s) 906, the latter (and possibly also the former)being coupled to the data communication network 902. The data inputdevice(s) 904 and the data source(s) 906 may collectively include thedevices 808, 810 and 812 discussed above with reference to FIG. 8. Moregenerally, the data input device(s) 904 and the data source(s) 906 mayencompass any and all devices conventionally used, or hereafter proposedfor use, in gathering, inputting, receiving and/or storing informationfor insurance company claim files.

Still further, FIG. 9 shows, as parts of the computer system 900,personal computer 908 assigned for use by one or more physicians (whomay be associated with the insurance company's long term disabilityinsurance program), automobile shop computer 909 (e.g., to transmitrepair estimates), and personal computers 910 assigned for use by casemanagers (who might also be associated with team leaders and/or claimhandlers the long term disability insurance program). The personalcomputers 908, 909, 910 may be coupled to the data communication network902.

Also included in the computer system 900, and coupled to the datacommunication network 902, is an electronic mail server computer 912.The electronic mail server computer 912 provides a capability forelectronic mail messages to be exchanged among the other devices coupledto the data communication network 902. Thus the electronic mail servercomputer 912 may be part of an electronic mail system included in thecomputer system 900. The computer system 900 may also be considered toinclude further personal computers (not shown), including, e.g.,computers which are assigned to individual claim handlers or otheremployees of the insurance company.

According to some embodiments, the rules driven claim processing engine901 uses a predictive model to facilitate a provisioning of claimhandlers. Note that the predictive model might be designed and/ortrained in a number of different ways. For example, FIG. 10 is a flowchart illustrating how a predictive model might be created according tosome embodiments. At 1002, data to be input to the predictive model maybe analyzed, scrubbed, and/or cleaned. This process might involve abroad review of the relevant variables that may be included in thesample data. Variables might be examined for the presence of erroneousvalues, such as incorrect data types or values that don't make sense.Observations with such “noisy” data or missing data may be removed fromthe sample. Similarly, any data points that represent outliers are alsomanaged.

At 1004, a data reduction process might be performed. This might occur,for example, between variables in the data sample and/or within specificvariables. According to some embodiments, certain variables may beassociated with one another and the number of these variables may bereduced. For example, it might be noted that back injuries should not behandled via an expedited workflow process. Within certain variables, theraw values may represent a level of information that is too granular.These raw values might then be categorized to reduce the granularity. Agoal of the data reduction process may be to reduce the dimensionalityof the data by extracting factors or clusters that may account for thevariability in the data.

At 1006, any necessary data transformations may be performed.Transformations of dependent and/or independent variables in statisticalmodels can be useful for improving interpretability, model fit, and/oradherence to modeling assumptions. Some common methods may includenormalizations of variables to reduce the potential effects of scale anddummy coding or other numeric transformations of character variables.

Once these steps are complete, the predictive model may be developed at1008. Depending on the nature of the desired prediction, variousmodeling techniques may be utilized and compared. The list ofindependent variables may be narrowed down using statistical methods aswell as business judgment. Lastly, the model coefficients and/or weightsmay be calculated and the model algorithm may be completed. For example,it might be determined that back injuries require a high degree ofmanagement (and thus, according to some embodiments, a back injury mightbe weighted more as compared to a shoulder injury and thus be morelikely to end up excluded from expedited workflow processing).

Note that many different types of data might be used to create,evaluate, and/or use a predictive model. For example, FIG. 11 is a blockdiagram of a system 1100 illustrating inputs to a predictive model 1110according to some embodiments. In this example, the predictive model1110 might receive information about prior insurance claims 1120 (e.g.,historical data). Moreover, the predictive model 1110 might receivemonetary information about claims 1130 (e.g., a total amount of paymentsmade in connection with a claim) and/or demographic information 1140(e.g., the age or sex of a claimant). According to some embodiments,claim notes 1150 are input to the predictive model 1110 (e.g., andkeywords may be extracted from the notes 1150). Other types ofinformation that might be provided to the predictive model 1110 includemedical bill information 1160 (e.g., including information about medicalcare that was provided to a claimant), disability details 1170 (e.g.,which part or parts of the body have been injured), and employment data1180 (e.g., an employee's salary or how long an employee has worked foran employer).

The predictive model 1110, in various implementation, may include one ormore of neural networks, Bayesian networks (such as Hidden Markovmodels), expert systems, decision trees, collections of decision trees,support vector machines, or other systems known in the art foraddressing problems with large numbers of variables. Preferably, thepredictive model(s) are trained on prior data and outcomes known to theinsurance company. The specific data and outcomes analyzed varydepending on the desired functionality of the particular predictivemodel 1110. The particular data parameters selected for analysis in thetraining process are determined by using regression analysis and/orother statistical techniques known in the art for identifying relevantvariables in multivariable systems. The parameters can be selected fromany of the structured data parameters stored in the present system,whether the parameters were input into the system originally in astructured format or whether they were extracted from previouslyunstructured text.

The present invention has been described in terms of several embodimentssolely for the purpose of illustration. Persons skilled in the art willrecognize from this description that the invention is not limited to theembodiments described, but may be practiced with modifications andalterations limited only by the spirit and scope of the appended claims.

What is claimed is:
 1. A system, comprising: an intake computer systemconfigured to receive data records at a rate of thousands of submissionsper year; and a rules driven processing engine for sequentially applyinga selected combination of filters to the data records to effect anexpedited processing data flow which reduces a number of data recordsprocessed according to a normal processing data flow, the rules drivenprocessing engine communicatively interposed between the intake computersystem and a plurality of handler computing devices, each correspondingto a different handler, and configured to: receive, from the intakecomputer system, one or more electronic files including the datarecords; reduce the data records to generate a first subset of reduceddata records by application of a first filter rule to the data recordsthat removes each of the data records that does not correspond to avalid policy; responsive to generation of the first subset of reduceddata records, reduce the first subset of data records to generate asecond subset of reduced data records by application of a second filterrule to the first subset of reduced data records that removes each ofthe data records of the first subset of reduced data records thatcorresponds to pre-determined legal jurisdictions; responsive togeneration of the second subset of reduced data records, reduce thesecond subset of reduced data records to generate a third subset ofreduced data records by application of a third filter rule to the secondsubset of reduced data records that removes each of the data records ofthe second subset of reduced data records that corresponds to apre-determined injury type, wherein the third subset of reduced datarecords comprises a final subset of reduced data records; automaticallytransmit the data records removed by sequential application of theselected combination of filters to a normal workflow module of aworkflow management system configured to assign each of the removed datarecords to a handler based upon a complexity of a claim corresponding tothe data record and handler workload, and transmit an electronic filecorresponding to each of the removed data records to one of theplurality of handler computing devices; automatically transmit the finalsubset of reduced data records not removed by sequential application ofthe selected combination of filters to an expedited workflow module ofthe workflow management system, the expedited workflow module configuredto: generate a payment for each of the data records of the final subsetof reduced data records which has a payment value below a pre-determinedthreshold; transmit each of the data records of the final subset ofreduced data records which has a payment value that is not below thepre-determined threshold to one of the plurality of handler computingdevices for processing by a handler who specializes in processing ofdata records transmitted to the expedited workflow module; and output,to a computing device, a display including data indicative of the finalsubset of reduced data records.
 2. The system of claim 1, wherein therules driven processing engine is further configured to: responsive togeneration of the third subset of reduced data records, reduce the thirdsubset of reduced data records to generate a fourth subset of reduceddata records by application of a fourth filter rule to the third subsetof reduced data records that removes each of the data records of thethird subset of reduced data records that corresponds to apre-determined policy type; and automatically transmit the data recordsremoved by application of the fourth filter rule to the normal workflowmodule of the workflow management system; wherein the fourth subset ofreduced data records comprises the final subset of reduced data recordstransmitted to the expedited workflow module of the workflow managementsystem.
 3. The system of claim 1, wherein the rules driven processingengine is further configured to: responsive to generation of the thirdsubset of reduced data records, reduce the third subset of reduced datarecords to generate a fourth subset of reduced data records byapplication of a fourth filter rule to the third subset of reduced datarecords that removes each of the data records of the third subset ofreduced data records that corresponds to a pre-determined thresholdmonetary amount; and automatically transmit the data records removed byapplication of the fourth filter rule to the normal workflow module ofthe workflow management system; wherein the fourth subset of reduceddata records comprises the final subset of reduced data recordstransmitted to the expedited workflow module of the workflow managementsystem.
 4. The system of claim 1, wherein the rules driven processingengine is further configured to: responsive to generation of the thirdsubset of reduced data records, reduce the third subset of reduced datarecords to generate a fourth subset of reduced data records byapplication of a fourth filter rule to the third subset of reduced datarecords that removes each of the data records of the third subset ofreduced data records that corresponds to a pre-determined liabilitydetermination; and automatically transmit the data records removed byapplication of the fourth filter rule to the normal workflow module ofthe workflow management system; wherein the fourth subset of reduceddata records comprises the final subset of reduced data recordstransmitted to the expedited workflow module of the workflow managementsystem.
 5. The system of claim 1, wherein the rules driven processingengine is further configured to: responsive to generation of the thirdsubset of reduced data records, reduce the third subset of reduced datarecords to generate a fourth subset of reduced data records byapplication of a fourth filter rule to the third subset of reduced datarecords that removes each of the data records of the third subset ofreduced data records that corresponds to a pre-determined geographiclocation; and automatically transmit the data records removed byapplication of the fourth filter rule to the normal workflow module ofthe workflow management system; wherein the fourth subset of reduceddata records comprises the final subset of reduced data recordstransmitted to the expedited workflow module of the workflow managementsystem.
 6. The system of claim 1, wherein the rules driven processingengine is further configured to automatically transmit information aboutthe third subset of data records to at least one of: (i) an emailserver, (ii) a report generator, and (iii) a calendar application. 7.The system of claim 1, wherein application of at least one of the firstfilter rule, the second filter rule, and the third filter rulecorresponds to receipt of data indicative of a First Notice Of Loss. 8.The system of claim 1, wherein at least one of the first filter rule,the second filter rule, and the third filter rule is based at least inpart on a predictive model trained by: applying one or both of avariable association data reduction process and a granularity reductionprocess to historical claim processing data records to generate reducedhistorical claim processing data records and reduce a dimensionality ofthe historical claim processing data records; and training, based uponthe reduced historical claim processing data records, the predictivemodel to generate exclusion filters for assignment of future datarecords corresponding to workers' compensation claims.
 9. A system,comprising: an intake computer system configured to receive data recordsreceived at a rate of thousands of submissions per year; and a rulesdriven processing engine for sequentially applying a selectedcombination of filters to the data records to effect an expeditedprocessing data flow which reduces a number of data records processedaccording to a normal processing data flow, the rules driven processingengine communicatively interposed between the intake computer system anda plurality of handler computing devices, each corresponding to adifferent handler the rules driven processing engine configured to:receive, by a processing engine communication device from the intakecomputer system, one or more electronic files including the datarecords; reduce the data records to generate a first subset of reduceddata records by application of a first filter rule to the data recordsthat removes each of the data records that does not correspond to avalid policy; responsive to generation of the first subset of reduceddata records, reduce the first subset of data records to generate asecond subset of reduced data records by application of a second filterrule to the first subset of reduced data records that removes each ofthe data records of the first subset of reduced data records thatcorresponds to pre-determined legal jurisdictions; responsive togeneration of the second subset of reduced data records, reduce thesecond subset of reduced data records to generate a third subset ofreduced data records by application of a third filter rule to the secondsubset of reduced data records that removes each of the data records ofthe second subset of reduced data records that corresponds to apre-determined injury type, wherein the third subset of reduced datarecords comprises a final subset of reduced data records; automaticallytransmit the data records removed by sequential application of theselected combination of filters to a normal workflow module of aworkflow management system configured to assign each of the removed datarecords to a handler based upon a complexity of a claim corresponding tothe data record and handler workload, and transmit an electronic filecorresponding to each of the removed data records to one of theplurality of handler computing devices, wherein the data records removedby sequential application of the selected combination of filterscomprise a reduced subset of the data records received from the intakecomputer system; and automatically transmit the final subset of reduceddata records not removed by sequential application of the selectedcombination of filters to an expedited workflow module of the workflowmanagement system, the expedited workflow module configured to generatea payment for each of the data records of the final subset of reduceddata records having a corresponding payment value below a pre-determinedthreshold, without each such data record being transmitted to one of theplurality of handler computing devices for processing by a handler. 10.The system of claim 9, wherein the rules driven processing engine isfurther configured to: responsive to generation of the third subset ofreduced data records, reduce the third subset of reduced data records togenerate a fourth subset of reduced data records by application of afourth filter rule to the third subset of reduced data records thatremoves each of the data records of the third subset of reduced datarecords that corresponds to a pre-determined disability type; andautomatically transmit the data records removed by application of thefourth filter rule to the normal workflow module of the workflowmanagement system; wherein the fourth subset of reduced data recordscomprises the final subset of reduced data records transmitted to theexpedited workflow module of the workflow management system.
 11. Thesystem of claim 9, wherein the rules driven processing engine is furtherconfigured to: responsive to generation of the third subset of reduceddata records, reduce the third subset of reduced data records togenerate a fourth subset of reduced data records by application of afourth filter rule to the third subset of reduced data records thatremoves each of the data records of the third subset of reduced datarecords that corresponds to a pre-determined indemnity paymentprobability; and automatically transmit the data records removed byapplication of the fourth filter rule to the normal workflow module ofthe workflow management system; wherein the fourth subset of reduceddata records comprises the final subset of reduced data recordstransmitted to the expedited workflow module of the workflow managementsystem.
 12. The system of claim 9, wherein the rules driven processingengine is further configured to: responsive to generation of the thirdsubset of reduced data records, reduce the third subset of reduced datarecords to generate a fourth subset of reduced data records byapplication of a fourth filter rule to the third subset of reduced datarecords that removes each of the data records of the third subset ofreduced data records that corresponds to a pre-determined compensabilityissue; and automatically transmit the data records removed byapplication of the fourth filter rule to the normal workflow module ofthe workflow management system; wherein the fourth subset of reduceddata records comprises the final subset of reduced data recordstransmitted to the expedited workflow module of the workflow managementsystem.
 13. The system of claim 9, wherein data corresponding to thedata records is received by the intake computer system via at least oneof: (i) a submitted paper claim, (ii) a telephone call center, and (iii)an online claim submission web page.
 14. The system of claim 9, whereinthe rules driven processing engine is further configured to output anindication of the third subset of data records to a team leadercomputing device.
 15. The system of claim 9, wherein application of atleast one of the first filter rule, the second filter rule, and thethird filter rule corresponds to receipt of data indicative of a FirstNotice Of Loss.
 16. The system of claim 9, wherein at least one of thefirst filter rule, the second filter rule, and the third filter rule isbased at least in part on a predictive model trained by: applying one orboth of a variable association data reduction process and a granularityreduction process to historical claim processing data records togenerate reduced historical claim processing data records and reduce adimensionality of the historical claim processing data records; andtraining, based upon the reduced historical claim processing datarecords, the predictive model to generate exclusion filters forassignment of future data records corresponding to workers' compensationclaims; wherein the predictive model is implemented by application of atleast one of: (i) a neural network, (ii) a Bayesian network, (iii) aHidden Markov model, (iv) an expert system, (v) a decision tree, (vi) acollection of decision trees, (vii) a support vector machine, and (viii)weighted factors.